Executive Overview
The landscape of digital commerce is undergoing its most radical transformation since the advent of mobile shopping. For decades, the playbook for launching and scaling an online store was clear: build a sleek storefront, secure top-tier search engine optimization (SEO) rankings on Google, capture mid-funnel intent via targeted keyword bidding, and retarget prospective buyers relentlessly through social media advertising.
Today, that well-worn playbook is being rewritten by the rapid rise of Large Language Models (LLMs), generative AI search engines, and agentic workflows. Consumers are increasingly bypassing traditional search engines altogether, turning instead to conversational AI platforms like ChatGPT, Claude, and Gemini to research, compare, and purchase products. This shift has triggered profound anxiety among digital marketers and ecommerce founders alike. How do you acquire customers when an algorithm is standing between your brand and the buyer?
To decode this turbulent new environment, recent industry discussions have turned to Kenny Trusnik, founder of Forest City Digital, a Cleveland-based marketing agency specializing in search, social, and retention. With a career background spanning corporate giants like Toyota and Sherwin-Williams before transitioning to high-growth startups, Trusnik offers a pragmatic, results-oriented lens on modern customer acquisition.
According to Trusnik, forward-thinking ecommerce brands are already seeing upwards of 10% of their total online revenue driven directly by LLM citations and referrals. However, capturing this traffic requires a fundamental shift in operational priorities. Success in the age of AI search is no longer just about keywords and backlinks; it is about deep, clean, structured product data and technical compliance with generative crawlers. This article explores Trusnik’s strategic blueprint for ecommerce startups navigating the AI tumult, breaking down actionable tactics across technical SEO, agentic storefronts, strategic product positioning, and high-impact marketing channels.
Detailed Chronology: From Corporate Background to AI-First Agency
Understanding Kenny Trusnik’s perspective requires examining the evolution of Forest City Digital and the philosophy that shaped its foundation. Founded in 2020, the agency emerged during a period of unprecedented volatility in digital retail, born out of the acceleration of ecommerce adoption catalyzed by the global pandemic.
The Foundation Years (2020)
When Trusnik established Forest City Digital in Cleveland, Ohio, the digital marketing landscape was already feeling the strain of rising customer acquisition costs (CAC) and saturated ad auctions. Prior to launching the agency, Trusnik’s professional trajectory took him through the structured corridors of corporate America at Toyota and Sherwin-Williams, before he pivoted to the fast-paced world of digital startups. In his startup tenure, he worked directly with content creators to help them monetize their viewership through integrated ecommerce storefronts.
This dual background—balancing the rigorous accountability of enterprise-level goal setting with the agility and consumer-centric focus of modern content-driven startups—imbued Forest City Digital with a distinct operational ethos.
"One thing I appreciated as a corporate employee was working toward real goals, regardless of the agencies involved," Trusnik explains. "I brought that approach to my agency: How do we tie marketing to what our clients are trying to accomplish?"
Instead of hiding behind vanity metrics such as impressions, click-through rates, or raw email open rates, Forest City Digital aligned its service offerings directly with bottom-line business outcomes: revenue growth, customer lifetime value (LTV), and sustainable profit margins.
The Pivot to Generative AI Optimization (2023–Present)
As generative AI tools proliferated across consumer markets, Trusnik and his team observed an immediate shift in consumer search behavior. Shoppers were no longer just typing fragmented keywords into a search bar; they were asking conversational engines complex, comparative questions. "Find me a sustainable, organic cotton hoodie under $100 with hidden pockets," or "What are the best non-alcoholic hemp beverages for a dinner party?"
Recognizing this behavior early, Forest City Digital integrated AI visibility into its core service offerings alongside traditional organic search (Google and Bing), social media management, and email retention. By auditing the backend infrastructure of Shopify merchants—particularly those in spec-intensive niches like aftermarket automotive and specialized hard goods—the agency began mapping out the exact data architectures required to make product catalogs readable, understandable, and ultimately recommendable by LLMs.
Supporting Context & Metrics: The Mechanics of AI Visibility and Agentic Storefronts
To understand why some ecommerce brands are thriving in the AI era while others are seeing their organic traffic evaporate, one must examine the technical underpinnings of how generative AI systems discover and recommend products.
The Rise of Agentic Storefronts
Earlier this year, commerce platform giant Shopify introduced a groundbreaking feature set known as Agentic Storefronts. This infrastructure is designed to expose a merchant’s complete product catalog directly to prominent LLMs, including OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini.
Trusnik likens the concept of Agentic Storefronts to historical data-syncing mechanisms like Google Merchant Center or Meta Commerce accounts, but with a vastly more sophisticated technical scope.
- Universal Catalog Exposure: Agentic Storefronts makes a merchant’s backend data accessible and crawlable by generative AI crawlers.
- Granular Product Fields: All Shopify product fields—ranging from basic categories, colors, and sizes to hyper-specific features, dimensions, and materials—are laid bare for algorithmic consumption.
- Contextual Comprehension: When a shopper prompts an LLM with a complex, multi-variable query, the AI queries these structured data environments to deliver tailored product recommendations rather than a generic list of blue links.
Structured Data and Schema Markup
Beyond platform-native tools like Agentic Storefronts, Trusnik emphasizes the critical importance of structured data, specifically Schema.org markup. While human visitors see images, persuasive copywriting, and customer reviews, AI crawlers rely heavily on structured code to interpret a website, its individual products, and its overarching brand purpose.
Without clean schema markup, even the most beautifully designed ecommerce site can remain invisible to a language model trying to synthesize options for a user.
The Crucial Technical Audit: Checking the Robots.txt File
For merchants looking to capture AI-driven traffic immediately, Trusnik highlights a surprisingly common pitfall: accidental blocking. Many older or poorly configured websites feature default or overly restrictive robots.txt files that block web crawlers from accessing the site.
A vital first step for any digital merchant is ensuring that their site’s robots.txt file does not inadvertently block crawlers from top generative AI platforms and search engines. If an AI crawler cannot access a site, the brand cannot be cited, referenced, or recommended—regardless of how superior its products might be.
Retention and the Sales Funnel
While AI visibility and organic search represent vital top-of-funnel acquisition channels, Forest City Digital stresses that acquisition is only half the battle. The agency leverages sophisticated email and SMS marketing platforms—such as Klaviyo and Brevo—to plug operational holes in client sales funnels. By nurturing first-time buyers through personalized automated flows, brands maximize customer lifetime value, offsetting the rising costs of paid acquisition.
Official Statements & Expert Insights: The 2026 Startup Playbook
If an ambitious entrepreneur were to launch an ecommerce company tomorrow, what would a winning playbook look like? During their recent strategy discussion, Eric Bandholz and Kenny Trusnik broke down the exact tactical steps required to build a resilient, high-growth brand in a landscape dominated by artificial intelligence.
1. Deep, Clean, Structured Product Data
According to Trusnik, the foundational priority for any new brand must be data hygiene.
"Focus on getting the product data as deep and clean as possible in a structured environment," Trusnik advises. "On Shopify, I would opt into Agentic Storefronts."
By ensuring that every single product attribute is meticulously categorized and tagged within a standardized framework, founders ensure their inventory is primed for algorithmic ingestion from day one.
2. Securing Citations in Listicles and "Best-Of" Articles
Traditional backlink acquisition focused heavily on domain authority and anchor text for Google’s PageRank algorithm. In the era of AI search, backlinks serve a different, equally critical purpose: feeding the training data and retrieval-augmented generation (RAG) systems that LLMs rely on.
"I would try to acquire links in prominent listicles or best-of articles," Trusnik notes. "Those are strong signals for the LLMs. With limited resources, I’d focus on structured product data and listicle links."
When an AI model is asked to recommend the top products in a specific category, it frequently crawls authoritative third-party review sites, industry roundups, and comparison articles. Being present in these curated digital spaces vastly increases the probability of being cited in conversational answers.
3. Deploying Paid Media for Top-of-Funnel Awareness
While organic optimization and AI visibility are essential for long-term margins, bootstrapping a brand purely through organic channels is increasingly difficult. If a startup is well-capitalized, Trusnik recommends a targeted paid media strategy:
"If I had the money, I would invest in Meta ads to build top-of-funnel awareness."
Paid social channels remain unparalleled for introducing novel physical products to cold audiences who are not yet actively searching for them.
4. Crafting Original, Non-Commoditized Content
The proliferation of generative AI has flooded the internet with low-quality, derivative content. By definition, AI-generated articles and social posts are repurposed aggregations of existing web data. To stand out, brands must produce genuinely original information.
"Content—articles, videos, social posts—helps if it contains original info unavailable elsewhere, unlike AI-generated content, which is, by definition, repurposed."
Whether it is proprietary industry research, deep behind-the-scenes product development stories, or expert commentary, originality is the ultimate moat against algorithmic irrelevance.
5. Navigating "Blue Oceans" and Iterative Novelty
When discussing product-market fit, Bandholz and Trusnik explored the delicate balance between radical innovation and iterative novelty.
"A product has to be novel and solve an unaddressed pain point," Trusnik explains, referencing classic blue ocean strategy principles.
However, entrepreneurs often make the mistake of trying to invent entirely new categories that consumers do not yet understand. Bandholz pointed to the massive success of Grüns gummies as a prime example of iterative novelty. Gummy vitamins were already a heavily commoditized, widely available product category. However, Grüns innovated iteratively by packing dozens of additional superfood ingredients into a single gummy, effectively repositioning daily vitamins as a premium superfood category.
Trusnik sees a parallel trend in another high-growth sector: hemp beverages.
"We work a lot with hemp beverage companies," Trusnik notes. "That space has exploded as an alternative to alcohol. It’s still hemp, but the novelty is that it replaces alcohol in social settings."
By taking an existing, well-understood consumer desire (relaxation, socializing) and offering a novel delivery mechanism that addresses a modern pain point (avoiding alcohol hangover and health risks), brands can carve out highly lucrative market segments without educating consumers from scratch.
Future Outlook: The Next Era of Digital Commerce
As we look toward the horizon of digital retail, the boundary between search, social, and commerce will continue to dissolve. The traditional customer journey—moving sequentially from a Google search to a static landing page to a transactional checkout—is being replaced by fluid, conversational commerce where artificial intelligence acts as an active shopping concierge.
For ecommerce founders and digital marketers, the takeaways from industry leaders like Kenny Trusnik are clear:
- Technical Rigor is Non-Negotiable: Clean backend metadata, properly formatted Schema.org markup, and open
robots.txtconfigurations are no longer optional "nice-to-haves" managed solely by back-end developers. They are frontline revenue drivers. - AI Platforms are Sales Channels: Treating ChatGPT, Claude, and Gemini as top-of-funnel referral engines requires the same strategic dedication historically reserved for Google Ads and SEO.
- Product and Data Quality Reign Supreme: In a world where AI can instantly generate marketing copy and summarize product specs, the underlying physical product and its structured digital representation are your ultimate differentiators.
Brands that adapt early to this algorithmic reality—optimizing their data deep-structures, earning authoritative third-party citations, and anchoring their product lines in true consumer utility—will not only survive the AI tumult; they will capture the lion’s share of tomorrow’s digital economy.
To learn more about Forest City Digital and their approach to modern ecommerce marketing, visit ForestCityDigital.com, or connect with Kenny Trusnik directly on LinkedIn to talk shop on all things ecommerce.
